Method and Apparatus for Acoustic Emissions Testing

ABSTRACT

An apparatus comprises an acoustic sensing system and an analyzer module. The acoustic sensing system is positioned relative to an object, wherein the acoustic sensing system detects acoustic emissions and generates acoustic waveform data for the acoustic emissions detected. The analyzer module is implemented in a computer system that receives load data and the acoustic waveform data for the object, generates a plurality of frequency distribution functions using the acoustic waveform data, and generates a frequency distribution function time evolution image containing a plurality of points of each of the plurality of frequency distribution functions.

CROSS-REFERENCE TO RELATED APPLICATION

This application is a continuation-in-part application of U.S. patentapplication Ser. No. 15/150,595, filed May 10, 2016.

BACKGROUND INFORMATION 1. Field

The present disclosure relates generally to acoustic emissions and, inparticular, to detecting acoustic emissions from objects. Still moreparticularly, the present disclosure relates to a method and apparatusfor analyzing acoustic emissions of objects to assess the structuralintegrity of these objects over time.

2. Background

Acoustic emission is the radiation of acoustic waves in an object ormaterial when the material undergoes a structural change. For example,without limitation, acoustic emissions may occur when a composite objectundergoes a structural change. This structural change may take the formof a crack forming, a crack extending, a split forming, a splitextending, delamination, some other type of structural change, or acombination thereof.

These acoustic waves may be detected using acoustic sensors that areused to generate data that can then be analyzed. However, identifyingthe nature or method of structural change with a desired level ofaccuracy using currently available methods for performing acousticemissions detection and analysis may be more difficult, tedious, andtime-consuming than desired. In some cases, identifying the nature ormode of structural change may not be possible using currently availablemethods.

Some currently available methods of acoustics emissions detection andtesting may require that the signals generated based on the acousticemissions detected be the result of a single type of structural event.However, some objects such as, but not limited to, composite objects,may simultaneously undergo multiple types of structural change. Somecurrently available methods of acoustic detection and testing may beunable to easily and quickly determine when multiple modes of structuralchange are occurring simultaneously. In particular, when multiplestructural changes occur in an object during a given time interval,currently available methods of acoustic detection and testing may beunable to identify the specific modes of structural change. Therefore,it would be desirable to have a method and apparatus that take intoaccount at least some of the issues discussed above, as well as otherpossible issues.

SUMMARY

In one illustrative embodiment, an apparatus is presented. The apparatuscomprises an acoustic sensing system and an analyzer module. Theacoustic sensing system is positioned relative to an object. Theacoustic sensing system detects acoustic emissions and generatesacoustic waveform data for the acoustic emissions detected. The analyzermodule is implemented in a computer system that receives load data andthe acoustic waveform data for the object, generates a plurality offrequency distribution functions using the acoustic waveform data, andgenerates a frequency distribution function time evolution imagecontaining a plurality of points of each of the plurality of frequencydistribution functions.

In another illustrative embodiment, a method for analyzing an objectusing acoustic waves is presented. Load data for the object is received.Acoustic waveform data for the object is received from an acousticsensing system, wherein the acoustic waveform data represents acousticemissions emanating from the object and is detected using the acousticsensing system. A plurality of frequency distribution functions isgenerated using the acoustic waveform data. A frequency distributionfunction time evolution image containing a plurality of points of eachof the plurality of frequency distribution functions is generated.

In yet another illustrative embodiment, a method is presented. Acousticemissions radiating from an object are detected using an acousticsensing system to generate acoustic waveform data. A plurality offrequency distribution functions are generated, by an analyzer module,using the acoustic waveform data, wherein each of the plurality offrequency distribution functions has an identifier of either arespective time or a respective load. An array is created that has aquantity of columns equivalent to a quantity of frequency distributionfunctions in the plurality of frequency distribution functions and aquantity of rows equivalent to a quantity of frequency bins in eachfrequency distribution function of the plurality of frequencydistribution functions. The array is filled such that a plurality ofpoints of each of the plurality of frequency distribution functions iscontained within the array, in which the array allows the analyzermodule to identify structural changes in the object previouslyunidentifiable by an operator using frequency plots containing a singlepoint for each waveform of the acoustic waveform data.

The features and functions can be achieved independently in variousembodiments of the present disclosure or may be combined in yet otherembodiments in which further details can be seen with reference to thefollowing description and drawings.

BRIEF DESCRIPTION OF THE DRAWINGS

The novel features believed characteristic of the illustrativeembodiments are set forth in the appended claims. The illustrativeembodiments, however, as well as a preferred mode of use, furtherobjectives and features thereof, will best be understood by reference tothe following detailed description of an illustrative embodiment of thepresent disclosure when read in conjunction with the accompanyingdrawings, wherein:

FIG. 1 is an illustration of a test environment in accordance with anillustrative embodiment;

FIG. 2 is an illustration of an object, an acoustic sensing system, andan analyzer module in the form of a block diagram in accordance with anillustrative embodiment;

FIG. 3 is an illustration of an isometric view of an aircraft inaccordance with an illustrative embodiment;

FIG. 4 is an illustration of a process for analyzing an object usingacoustic emissions in the form of a flowchart in accordance with anillustrative embodiment;

FIG. 5 is an illustration of a process for generating a plurality offrequency distribution functions in the form of a flowchart inaccordance with an illustrative embodiment;

FIG. 6 is an illustration of one process, in the form of a flowchart,for applying a set of learning algorithms to a plurality of frequencydistribution functions to generate an output in accordance with anillustrative embodiment;

FIG. 7 is an illustration of another process, in the form of aflowchart, for applying a set of learning algorithms to a plurality offrequency distribution functions to generate an output in accordancewith an illustrative embodiment;

FIG. 8 is an illustration of a process for analyzing a composite objectin an aircraft using acoustic emissions in the form of a flowchart inaccordance with an illustrative embodiment;

FIG. 9 is an illustration of a data processing system in the form of ablock diagram in accordance with an illustrative embodiment;

FIG. 10 is an illustration of an aircraft manufacturing and servicemethod in the form of a block diagram in accordance with an illustrativeembodiment;

FIG. 11 is illustration of an aircraft in the form of a block diagram inaccordance with an illustrative embodiment;

FIG. 12 is an illustration of a frequency distribution function timeevolution image in accordance with an illustrative embodiment;

FIG. 13 is an illustration of an object, an acoustic sensing system, andan analyzer module in the form of a block diagram in accordance with anillustrative embodiment;

FIG. 14 is an illustration of a difference in change graph that may beused to tune parameters in accordance with an illustrative embodiment;

FIG. 15 is an illustration of a frequency distribution function timeevolution image beside a normalized loading curve in accordance with anillustrative embodiment;

FIG. 16 is an illustration of a plot of singular values from singularvalue decomposition of a frequency distribution function time evolutionimage in accordance with an illustrative embodiment;

FIG. 17 is an illustration of a flowchart of a method for analyzing anobject using acoustic waves in accordance with an illustrativeembodiment;

FIG. 18 is an illustration of a flowchart of a method for creating anarray for analysis in accordance with an illustrative embodiment; and

FIG. 19 is an illustration of a flowchart of a method for turningparameters in accordance with an illustrative embodiment.

DETAILED DESCRIPTION

The illustrative embodiments take into account different considerations.For example, the illustrative embodiments take into account that it maybe desirable to have a method and apparatus for detecting and analyzingacoustic emissions from objects that enable the identification andclassification of multiple structural events that are occurringsimultaneously. In particular, the illustrative embodiments take intoaccount that it may be desirable to have a method and apparatus foranalyzing acoustic emissions relative to the load history of an objectthat allows for accurate correlation between acoustic waveforms andspecific modes of structural change.

Thus, the illustrative embodiments provide a method and apparatus foranalyzing an object using acoustic emissions. In one illustrativeexample, acoustic emissions emanating from the object are detected usingan acoustic sensing system to generate acoustic waveform data. Theacoustic waveform data is received along with load data for the object.A plurality of bins is created for the load data. A plurality offrequency distribution functions is generated for the plurality of binsusing the acoustic waveform data. A set of learning algorithms isapplied to the plurality of frequency distribution functions to generatean output that allows an operator to more easily and quickly assess astructural integrity of the object.

In particular, the illustrative embodiments provide a method andapparatus that solves the challenges associated with determining whenmultiple modes of structural change occur in an object simultaneously.Further, the illustrative embodiments provide a method and apparatusthat solves the challenges associated with identifying each specificmode of structural change that occurs in an object during a given timeinterval even when multiple modes of structural change occur during thattime interval.

The illustrative embodiments take into account that in traditionalfrequency plots for acoustic emission data, such as point plots ofmaximum frequency versus time, some signal features are masked or lost.The illustrative embodiments take into account that unidentified peakscorrelate to unidentified structural changes.

The illustrative embodiments provide a method and apparatus thatidentify structural changes previously unidentified by operators inconventional processes. The illustrative embodiments, in some cases,provide a method and apparatus to more efficiently use acoustic waveformdata to identify previously unidentified structural changes. Theillustrative embodiments provide a method and apparatus to moreefficiently find patterns in acoustic waveform data to identifypreviously unidentified structural changes.

The illustrative embodiments provide a method and apparatus that willreduce testing costs and reduce cycle time for manufacturing components.The illustrative embodiments provide a method and apparatus that may beused in developing new materials. Behavioral characteristics andstructural changes of the new materials identified by the illustrativeembodiments will have greater certainty than conventional methods.

Referring now to the figures and, in particular, with reference to FIG.1, an illustration of a test environment is depicted in accordance withan illustrative embodiment. In this illustrative example, testenvironment 100 may be used to perform testing of object 102. In thisillustrative example, object 102 takes the form of a composite object.However, in other illustrative examples, object 102 may be some othertype of object, such as, but not limited to, a metallic object.

Acoustic sensing system 104 is used to detect acoustic emissionsemanating from object 102. Acoustic sensing system 104 includes acousticsensors 106, signal conditioner 107, and transmitter 108. Each acousticsensor of acoustic sensors 106 is positioned in contact with object 102and is capable of detecting acoustic waves that may radiate throughobject 102 over time as a load is applied to object 102. This load (notshown) may be constant over time, may vary over time, or may follow apattern of constant intervals mixed with varying intervals over time.

In this illustrative example, acoustic sensors 106 generate acousticemissions signals that are sent through signal conditioner 107 totransmitter 108. Signal conditioner 107 may amplify, filter, bothamplify and filter these acoustic emissions signals. Transmitter 108 maythen convert the acoustic emissions signals into acoustic waveform datathat is then wirelessly transmitted to analyzer module 109 forprocessing. In some cases, transmitter 108 includes a preamplifier oramplifier component that may adjust the gain of the acoustic emissionssignals before conversion into the acoustic waveform data.

As depicted, analyzer module 109 is implemented in computer system 110.In this illustrative example, transmitter 108 wirelessly sends theacoustic waveform data to analyzer module 109 in computer system 110. Inother illustrative examples, transmitter 108 may send the acousticwaveform data to analyzer module 109 over one or more wired connections.

Analyzer module 109 receives both the acoustic waveform data and loaddata. The load data may include measurements of the load being appliedto or the load being experienced by object 102 over time. Analyzermodule 109 processes the acoustic waveform data and the load data in amanner that reduces the amount of time and computer processing resourcesneeded to identify the nature and modes of structural change in object102 over time based on the acoustic emissions detected. In particular,analyzer module 109 generates an output that allows an operator to moreeasily and quickly assess a structural integrity of object 102.

With reference now to FIG. 2, an illustration of an object, an acousticsensing system, and an analyzer module is depicted in the form of ablock diagram in accordance with an illustrative embodiment. Object 200may take a number of different forms. In one illustrative example,object 200 takes the form of composite object 202. However, in otherillustrative examples, object 200 may take the form of a metal object,an object having at least a partial metallic composition, or some othertype of object.

Depending on the implementation, object 200 may be at any stage in thelifecycle of object 200. For example, without limitation, object 200 maybe in a testing stage, in a system integration stage, in an in-servicestage, in a maintenance stage, in a repair stage, or at some other pointin time during the lifecycle of object 200. In one illustrative example,composite object 202 may be a composite test coupon. Object 102 in FIG.1 is an example of one implementation for object 200 in FIG. 2.

Acoustic sensing system 204 is used to detect acoustic emissions 206from object 200 in response to the loading of object 200. This loadingmay be performed in a number of different ways, depending on theimplementation. For example, in some cases, an external load may beapplied to object 200 for an extended period of time, while acousticsensing system 204 is used to detect acoustic emissions 206 that resultdue to this loading. In other illustrative examples, the loading may bedue to the integration of object 200 into a larger structure or system.

The loading of object 200 may affect the structural integrity of object200 over time. For example, the loading may cause certain structuralchanges in object 200 that reduce the structural integrity of object200. These structural changes may include, but are not limited to, crackformation, splitting, the extension of cracks, the extension of splits,fiber breakage, delamination, some other type of undesired structuralchange, or a combination thereof.

Acoustic emissions 206 are acoustic waves that radiate through object200 due to structural changes in object 200. Acoustic sensing system 204comprises set of acoustic sensors 208. As used herein, a “set of” itemsmay include one or more items. In this manner, set of acoustic sensors208 may include one or more acoustic sensors.

Acoustic sensor 210 is an example of one acoustic sensor in set ofacoustic sensors 208. In one illustrative example, acoustic sensor 210is positioned in contact with object 200 to detect acoustic emissions206.

Set of acoustic sensors 208 detect acoustic emissions 206 and generateacoustic waveform data 212 for acoustic emissions 206 detected. Acousticwaveform data 212 is sent to analyzer module 214. Analyzer module 214may receive acoustic waveform data 212 from acoustic sensing system 204using any number of wired communications links, wireless communicationslinks, other types of communications links, or a combination thereof.

In this illustrative example, analyzer module 214 may be implemented insoftware, hardware, firmware, or a combination thereof. When software isused, the operations performed by analyzer module 214 may be implementedusing, for example, without limitation, program code configured to runon a processor unit. When firmware is used, the operations performed byanalyzer module 214 may be implemented using, for example, withoutlimitation, program code and data and stored in persistent memory to runon a processor unit.

When hardware is employed, the hardware may include one or more circuitsthat operate to perform the operations of analyzer module 214. Dependingon the implementation, the hardware may take the form of a circuitsystem, an integrated circuit, an application specific integratedcircuit (ASIC), a programmable logic device, or some other suitable typeof hardware device configured to perform any number of operations.

A programmable logic device may be configured to perform certainoperations. The device may be permanently configured to perform theseoperations or may be reconfigurable. A programmable logic device maytake the form of, for example, without limitation, a programmable logicarray, a programmable array logic, a field programmable logic array, afield programmable gate array, or some other type of programmablehardware device.

In this illustrative example, analyzer module 214 is implemented usingcomputer system 216. Analyzer module 109 implemented in computer system110 in FIG. 1 may be an example of one implementation for analyzermodule 214 implemented in computer system 216. Computer system 216 mayinclude a single computer or multiple computers in communication witheach other.

In addition to receiving acoustic waveform data 212, analyzer module 214also receives load data 218. In one illustrative example, load data 218may be data generated by load sensing system 220. Load sensing system220 may include one or more load sensors that measure the loading ofobject 200 over time.

In other illustrative examples, analyzer module 214 retrieves load data218 from database 222. For example, without limitation, load data 218may be previously generated load data that was generated for an objectsimilar to object 200 under the same or similar loading conditions.

Analyzer module 214 creates plurality of bins 224 for load data 218.Plurality of bins 224 has plurality of bin widths 226. In particular,each bin in plurality of bins 224 has a corresponding bin width inplurality of bin widths 226. In one illustrative example, plurality binwidths 226 may be equal. However, in other illustrative examples, one ormore bin widths of plurality of bin widths 226 may be different.

In some illustrative examples, plurality of bin widths 226 is aplurality of time-based bin widths. In other words, each bin ofplurality of bins 224 may correspond to a time interval. In otherillustrative examples, plurality of bin widths 226 is a plurality ofload-based bin widths. In other words, each bin of plurality of bins 224may correspond to a load interval.

Analyzer module 214 generates plurality of frequency distributionfunctions 228 using plurality of bins 224 and acoustic waveform data212. In one illustrative example, plurality of frequency distributionfunctions 228 takes the form of plurality of frequency histograms 230.

Plurality of frequency distribution functions 228 includes one frequencydistribution function for each bin in plurality of bins 224. Forexample, analyzer module 214 generates frequency distribution function232 for bin 234. Bin 234 has a defined bin width that may be a definedtime interval or a defined load interval.

In one illustrative example, analyzer module 214 creates frequencydistribution function 232 by dividing a selected frequency range intoplurality of frequency bins 235. Depending on the implementation,plurality of frequency bins 235 may have the same or different binwidths. Each frequency bin in plurality of frequency bins 235 is used tohold a count and therefore can be incremented.

Analyzer module 214 then processes acoustic waveform data 212 relativeto load data 218. For example, for each bin in plurality of bins 224,analyzer module 214 identifies a set of waveforms that fall within thatbin using acoustic waveform data 212. Thereafter, analyzer module 214computes a Fast Fourier Transform for the set of waveforms identifiedfor each bin in plurality of bins 224.

As one illustrative example, analyzer module 214 identifies set ofwaveforms 238 that falls within bin 234 using acoustic waveform data212. In some illustrative examples, plurality of bin widths 226 forplurality of bins 224 may be selected such that at least one waveformfalls entirely within each bin of plurality of bins 224. Next, analyzermodule 214 computes a Fast Fourier Transform for set of waveforms 238that falls within bin 234. Analyzer module 214 then identifies frequencypeaks 240 for set of waveforms 238 based on the Fast Fourier Transformcomputed.

In one illustrative example, analyzer module 214 selects a definednumber of frequency peaks for each waveform in set of waveforms 238. Asused herein, a “number of” items may include one or more items. In thismanner, a defined number of frequency peaks may include one or morefrequency peaks. In some cases, the number of frequency peaks selectedby analyzer module 214 may be, for example, without limitation, three,four, five, eight, or some other number of frequency peaks for eachwaveform in set of waveforms 238 based on the Fast Fourier Transformercomputed for set of waveforms 238.

Analyzer module 214 increments a corresponding frequency bin inplurality of frequency bins 235 when a frequency peak that has beenidentified falls within the corresponding frequency bin. For example, ifany of frequency peaks 240 falls within the frequency bin correspondingto the range of about 80 kilohertz to about 90 kilohertz, then thefrequency bin is incremented by the total number of frequency peaksfalling within this range. This process creates frequency distributionfunction 232 for bin 234.

In other illustrative examples, plurality of frequency bins 235 may beaccumulated differently. For example, a frequency bin in plurality offrequency bins 235 may be an accumulation of energy at that frequencybin, computed using acoustic waveform data 212.

The process of creating frequency distribution function 232 for bin 234is repeated for each of plurality of bins 224 to ultimately createplurality of frequency distribution functions 228. Plurality offrequency distribution functions 228 provide an operator with an easyway to quickly assess the structural integrity of object 200.

When object 200 is a test object, further processing of plurality offrequency distribution functions 228 is performed by analyzer module214. For example, without limitation, analyzer module 214 createsplurality of clusters 242 using plurality of frequency distributionfunctions 228. Plurality of clusters 242 is a plurality of clusters ofinterest.

In one illustrative example, analyzer module 214 applies one or moreunsupervised learning algorithms to plurality of frequency distributionfunctions 228 to establish plurality of clusters 242. Each cluster inplurality of clusters 242 is a grouping of frequency distributionfunctions from plurality of frequency distribution functions 228.

As used herein, an unsupervised learning algorithm is a machine learningalgorithm for drawing inferences from datasets comprising data withoutlabeled responses. One example of unsupervised learning is clustering. Aclustering algorithm may be an algorithm for grouping a set of elementsin such a way that elements in the same group, which may be referred toas a cluster, are more similar to each other than to those in othergroups.

In these illustrative examples, analyzer module 214 may use a set ofunsupervised learning algorithms to group frequency distributionfunctions in plurality of frequency distribution functions 228 to formplurality of clusters 242. Depending on the implementation, a k-meansclustering algorithm, a mixture model clustering algorithm, ahierarchical clustering algorithm, some other type of clusteringalgorithm, some other type of unsupervised learning algorithm, or acombination thereof may be used to identify plurality of clusters 242.

Each cluster in plurality of clusters 242 corresponds to a structuralchange that affects the structural integrity of object 200. In oneillustrative example, each cluster in plurality of clusters 242corresponds to a different mode of structural change that reduces thestructural integrity of object 200.

In one illustrative example, analyzer module 214 identifies plurality ofdescriptors 244 for plurality of clusters 242. A descriptor for acluster may be a centroid, a mean, or some other type of representativefrequency distribution function for the cluster. As one illustrativeexample, the descriptor may be the centroid frequency distributionfunction for that cluster.

Plurality of clusters 242 may be associated with a plurality of modes ofstructural change using alternate test data 236. Alternate test data 236may be data from which structural changes in object 200 may be readilyidentified. For example, alternate test data 236 may take the form ofx-ray imaging data, ultrasound imaging data, infrared imaging data,modeling data, or some other type of data. The modeling data may begenerated from a computer model.

As one illustrative example, without limitation, alternate test data 236takes the form of in-situ x-ray data generated for object 200 during theloading of object 200. Alternate test data 236 is then used to detectstructural changes in object 200 and identify these structural changesas plurality of modes 246. Each mode in plurality of modes 246 may be adifferent type of structural change. In some cases, each mode inplurality of modes 246 may be referred to as a mode of structuralcompromise.

For example, without limitation, when object 200 takes the form of acomposite test coupon, plurality of modes 246 may include crackformation, crack extension, splitting, and split extension. In somecases, plurality of modes 246 may also include fiber breakage,delamination, or some other form of structural compromise.

Both plurality of modes 246 and plurality of clusters 242 are mappedback to load data 218 such that each cluster in plurality of clusters242 substantially overlaps with a corresponding mode in plurality ofmodes 246. In other words, plurality of modes 246 may be mapped backedto specific times, load conditions, or both using load data 218.

Similarly, plurality of clusters 242 may be mapped back to specifictimes, load conditions, or both using load data 218. For example,without limitation, each bin in plurality of bins 224 for load data 218may be designated as holding one or more waveforms that belong to aparticular cluster in plurality of clusters 242.

In one illustrative example, each cluster of plurality of clusters 242may substantially overlap, or overlap within selected tolerances, with acorresponding mode in plurality of modes 246 with respect to time. Inthis manner, each cluster in plurality of clusters 242 may be pairedwith or assigned to a corresponding mode in plurality of modes 246. Inone illustrative example, the descriptor corresponding to each clusterin plurality of clusters 242 is paired with a corresponding mode inplurality of modes 246. In other words, plurality of descriptors 244 maybe paired with plurality of modes 246.

In one illustrative example, plurality of clusters 242 may include afirst cluster having a first descriptor, a second cluster having asecond descriptor, a third cluster having a third descriptor, and afourth cluster having a fourth descriptor. In this illustrative example,the first cluster and the first descriptor represent a first mode ofstructural change. The second cluster and the second descriptorrepresent a second mode of structural change. The third cluster and thethird descriptor represent a third mode of structural change. The fourthcluster and the fourth descriptor represent a fourth mode of structuralchange. Of course, in other illustrative examples, plurality of clusters242 may include fewer than four clusters or more than four clusters.

Once each cluster in plurality of clusters 242 has been associated witha corresponding mode of structural change, plurality of descriptors 244for plurality of clusters 242 is stored for future use. For example,plurality of descriptors 244 may be stored in database 222, or someother type of data structure or data storage, along with the modeclassification for each descriptor.

In one illustrative example, analyzer module 214 generates descriptorclassification output 248 that identifies the pairing of each mode inplurality of modes 246 with a corresponding descriptor in plurality ofdescriptors 244. Descriptor classification output 248 may be stored indatabase 222, or in some other data structure or data storage, forfuture use. In this manner, descriptor classification output 248establishes baseline data that may be used to evaluate the structuralintegrity of one or more parts that are structurally the same as orstructurally similar to object 200.

In other illustrative examples, object 200 may not be a test object.Rather, object 200 may be at an in-service stage, a maintenance stage, arepair stage, a certification stage, or some other type of stage in thelifecycle of object 200. In these illustrative examples, once pluralityof frequency distribution functions 228 has been generated, analyzermodule 214 applies one or more supervised learning algorithms toplurality of frequency distribution functions 228.

As used herein, a supervised learning algorithm is a machine learningalgorithm for drawing inferences from labeled training data. In theseillustrative examples, this labeled training data takes the form ofdescriptor classification output 248 that labels each descriptor inplurality of descriptors 244, which corresponds with a cluster inplurality of clusters 242, with a corresponding mode of plurality ofmodes 246.

A support vector machine is an example of one type of supervisedlearning algorithm. For example, without limitation, a support vectormachine may be applied to plurality of frequency distribution functions228 and stored plurality of descriptors 247 to generate classificationoutput 250. Stored plurality of descriptors 247 is generated in a mannersimilar to plurality of descriptors 244. Stored plurality of descriptors247 may be stored in database 222 or some other type of data structureor data storage.

In particular, a binary decision is made for each bin in plurality ofbins 224 based on stored plurality of descriptors 247. Morespecifically, the frequency distribution function generated for each binin plurality of bins 224 is analyzed relative to each descriptor inplurality of descriptors 244.

For example, without limitation, frequency distribution function 232 forbin 234 may be analyzed relative to each descriptor in stored pluralityof descriptors 247. A determination is made as to whether frequencydistribution function 232 matches the descriptor within selectedtolerances or not. If frequency distribution function 232 matches thedescriptor within selected tolerances, then set of waveforms 238 thatfall within bin 234 may be classified as representing the mode thatcorresponds to that descriptor. This decision is performed for eachdescriptor in stored plurality of descriptors 247.

Because this type of binary decision is being made for each descriptorin stored plurality of descriptors 247, the each bin in plurality ofbins 224 may be classified as representing multiple modes of structuralchange. In this manner, the set of waveforms that fall within any givenbin of plurality of bins 224 may be classified as representing one ormore modes of structural change. In some cases, the set of waveforms ina particular bin may be determined to not represent any particular modein plurality of modes 246.

In one illustrative example, analyzer module 214 generatesclassification output 250 that includes a classification of each bin inplurality of bins 224 using one or more modes of plurality of modes 246based on the analysis described above. In other illustrative examples,analyzer module 214 generates classification output 250 that identifiesthe classification of each waveform in acoustic waveform data 212 usingone or more modes of plurality of modes 246.

Thus, the illustrative embodiments provide an accurate and efficientmethod for assessing the structural integrity of object 200. Theinformation obtained based on this type of assessment may be used tothen make decisions about object 200 with respect to certification,maintenance, repair, system integration, some other type of task, or acombination thereof.

The processing performed by analyzer module 214 may be easily tailoredfor different types of objects and loading conditions. As oneillustrative example, plurality of bin widths 226 may be selected basedon the type of loading of object 200. For example, without limitation,when object 200 is loaded more quickly, acoustic emissions 206 may occurmore rapidly. Plurality of bin widths 226 may be selected to createsmaller bins to allow for clearer separation of events. However, whenobject 200 is loaded more slowly, acoustic emissions 206 may occur moreslowly. Plurality of bin widths 226 may then be selected to createlarger bins to thereby reduce the overall volume of data that needs tobe processed.

Further, in some illustrative examples, analyzer module 214 may beconfigured to display descriptor classification output 248,classification output 250, or both through a graphical user interface ondisplay system 252. In some cases, plurality of frequency distributionfunctions 228 may be displayed on display system 252. In this manner, anoperator may able to quickly and easily make decisions about object 200.

The illustration of object 200, acoustic sensing system 204, andanalyzer module 214 in FIG. 2 is not meant to imply physical orarchitectural limitations to the manner in which an illustrativeembodiment may be implemented. Other components in addition to or inplace of the ones illustrated may be used. Some components may beoptional. Also, the blocks are presented to illustrate some functionalcomponents. One or more of these blocks may be combined, divided, orcombined and divided into different blocks when implemented in anillustrative embodiment.

For example, without limitation, in some cases, acoustic sensing system204 may include at least one signal conditioner (not shown), such assignal conditioner 107 in FIG. 1, and a transmitter (not shown), such astransmitter 108 in FIG. 1. As one illustrative example, a signalconditioner may be used to amplify and filter the frequency content ofthe acoustic emissions signal detected by acoustic sensor 210. Theacoustic emissions signal may then be converted into acoustic waveformdata 212 by a transmitter sends acoustic waveform data 212 to analyzermodule 214. The transmitter may send acoustic waveform data 212 toanalyzer module 214 using one or more wireless communications links,wired communications links, or other type of communications links.

In some cases, a single signal conditioner may be used for amplifyingand filtering the set of acoustic emissions signals generated by set ofacoustic sensors 208. In other illustrative examples, each acousticsensor in set of acoustic sensors 208 may be connected to a differentsignal conditioner. In still other illustrative examples, a signalconditioner may be integrated as part of each acoustic sensor in set ofacoustic sensors 208.

Further, although classification output 250 is described as beinggenerated using one or more supervised learning algorithms, in otherillustrative examples, a semi-supervised learning algorithm or a processthat combines supervised and unsupervised learning may be used togenerate classification output 250. Still further, although descriptorclassification output 248 is described as being generated using one ormore unsupervised learning algorithms, in other illustrative examples, asemi-supervised learning algorithm or a process that combines supervisedand unsupervised learning may be used to generate descriptorclassification output 248.

With reference now to FIG. 3, an illustration of an isometric view of anaircraft is depicted in accordance with an illustrative embodiment. Inthis illustrative example, aircraft 300 includes wing 302 and wing 304attached to fuselage 306. Aircraft 300 also includes engine 308 attachedto wing 302 and engine 310 attached to wing 304.

Further, aircraft 300 includes tail section 312. Horizontal stabilizer314, horizontal stabilizer 316, and vertical stabilizer 318 are attachedto tail section 312.

An acoustic sensing system (not shown), such as acoustic sensing system204 in FIG. 2 or acoustic sensing system 104 in FIG. 1, may bepositioned relative to aircraft 300 to monitor the acoustic emissions ofvarious parts of aircraft 300 during the lifecycle of aircraft 300. Forexample, without limitation, the acoustic sensing system may includevarious acoustic sensors (not shown) at locations 320 along aircraft300. Locations 320 may include locations that are in contact with asurface of a part of aircraft 300, embedded within a part or structureof aircraft 300, positioned near but not in contact with a part orstructure of aircraft 300, or a combination thereof.

At any stage during the lifecycle of aircraft 300, the acoustic waveformdata generated by the acoustic sensing system 204 may be collected andanalyzed by analyzer module 214 in FIG. 2. In this manner, thestructural integrity of the various parts or structures of aircraft 300may be analyzed and any detected undesired structural changes may beclassified.

With reference now to FIG. 4, an illustration of a process for analyzingan object using acoustic emissions is depicted in the form of aflowchart in accordance with an illustrative embodiment. The processillustrated in FIG. 4 may be implemented by analyzer module 214described in FIG. 2.

The process may begin by receiving acoustic waveform data for an objectfrom an acoustic sensing system in which the acoustic waveform datarepresents acoustic emissions emanating from the object as detected bythe acoustic sensing system (operation 400). Next, load data for theobject is received (operation 402). Thereafter, a plurality of bins iscreated for the load data (operation 404).

In operation 404, depending on the implementation, the plurality of binsmay be a plurality of time bins or a plurality of load bins. A pluralityof frequency distribution functions is then generated for the pluralityof bins using the acoustic waveform data (operation 406). In operation406, a frequency distribution function is generated for each bin in theplurality of bins. In some illustrative examples, the plurality offrequency distribution functions take the form of a plurality offrequency histograms.

Thereafter, a set of learning algorithms is applied to the plurality offrequency distribution functions and the acoustic waveform data togenerate an output that allows an operator to more easily and quicklyassess a structural integrity of the object (operation 408), with theprocess terminating thereafter. The process described in FIG. 4 mayreduce the overall time, effort, and computer-based processing resourcesthat are needed to accurately assess the structural integrity of theobject when the object is subject to multiple modes of structural changeoccurring simultaneously.

With reference now to FIG. 5, an illustration of a process forgenerating a plurality of frequency distribution functions is depictedin the form of a flowchart in accordance with an illustrativeembodiment. The process illustrated in FIG. 5 may be implemented byanalyzer module 214 described in FIG. 2. This process may be used toimplement operation 406 in FIG. 4.

The process begins by creating a plurality of frequency bins (operation500). In operation 500, each bin in the plurality of frequency bins mayhave a defined bin width. The bin widths of plurality of frequency binsmay be the same or may be different. In one illustrative example,operation 500 is performed by dividing a selected frequency range intothe plurality of frequency bins based on a defined frequency interval.

Thereafter, a bin is selected from the plurality of bins for processing(operation 502). In operation 502, the plurality of bins may be, forexample, the plurality of bins created in operation 404 in FIG. 4.

Next, a set of waveforms that fall within the bin that is selected isidentified (operation 504). A Fast Fourier Transform (FFT) is computedfor the set of waveforms (operation 506). A number of frequency peaks isidentified for each waveform in the set of waveforms (operation 508).Each frequency bin in the plurality of frequency bins within which afrequency peak falls is incremented (operation 510). In this manner, afrequency distribution function is created for the selected bin.Operation 510 is one example of how the plurality of frequency bins maybe updated based on the Fast Fourier Transform computed in operation 506and the number of frequency peaks identified for each waveform in theset of waveforms identified in operation 508.

A determination is then made as to whether any additional bins need tobe processed (operation 512). If no additional bins need to beprocessed, the process terminates. Otherwise, the process returns tooperation 502 described above. The process described in FIG. 5 resultsin the generation of a plurality of frequency distribution functions forthe plurality of bins.

With reference now to FIG. 6, an illustration of one process forapplying a set of learning algorithms to a plurality of frequencydistribution functions to generate an output is depicted in the form ofa flowchart in accordance with an illustrative embodiment. The processillustrated in FIG. 6 may be implemented by analyzer module 214 in FIG.2 and may be one example of how operation 408 in FIG. 4 may beimplemented.

The process may begin by applying a set of unsupervised learningalgorithms to a plurality of frequency distribution functions toestablish a plurality of clusters (operation 600). In operation 600, theplurality of frequency distribution functions are grouped into clustersbased on the unsupervised learning algorithms.

Next, a plurality of descriptors is identified for the plurality ofclusters (operation 602). In operation 602, a descriptor is identifiedfor each cluster. The descriptor is a representative frequencydistribution function for the cluster. The descriptor for a particularcluster may be, for example, without limitation, a centroid frequencydistribution function or a mean frequency distribution function for thatcluster.

Thereafter, each descriptor in the plurality of descriptors isassociated with a particular mode of structural change based on theidentification of a plurality of modes using alternate test data(operation 604). In operation 604, the alternate test data may be, forexample, x-ray data. Further, the plurality of modes may include, forexample, without limitation, fiber breakage, splitting, split extension,delamination, crack formation, crack extension, or some other mode ofstructural change.

A descriptor classification output that pairs each descriptor of theplurality of descriptors with a particular mode of the plurality ofmodes is generated (operation 606), with the process terminatingthereafter. This descriptor classification output may then be used toperform evaluation of the structural integrity of other objects.

With reference now to FIG. 7, an illustration of another process forapplying a set of learning algorithms to a plurality of frequencydistribution functions to generate an output is depicted in the form ofa flowchart in accordance with an illustrative embodiment. The processillustrated in FIG. 7 may be implemented by analyzer module 214 in FIG.2 and may be another example of how operation 408 in FIG. 4 may beimplemented.

The process may begin by applying a set of supervised learningalgorithms to a plurality of frequency distribution functions and aplurality of descriptors (operation 700). A frequency distributionfunction is selected from the plurality of frequency distributionfunctions (operation 702). Each frequency distribution function of theplurality of frequency distribution functions represents a set ofwaveforms that fall within a particular time bin or load bin based onload data.

Next, a descriptor is selected from a stored plurality of descriptors(operation 704). In operation 704, the stored plurality of descriptorsmay be previously identified for previously generated acoustic waveformdata in a manner similar to the process described in FIG. 6. Eachdescriptor in the stored plurality of descriptors corresponds to adifferent mode of structural change.

Thereafter, the frequency distribution function selected is analyzedrelative to the descriptor selected (operation 706). For example, inoperation 706, the frequency distribution function may be compared tothe descriptor, which may be a representative frequency distributionfunction for a cluster.

Next, the frequency distribution function is given a binary classifiervalue based on the analysis (operation 708). In operation 708, thebinary classifier value may be either a first value or a second value.For example, the first value may indicate that the frequencydistribution function does match the descriptor within selectedtolerances, while the second value may indicate that the frequencydistribution function does not match the descriptor within selectedtolerances. In some cases, the first value and the second value may bereferred to as a positive classification value and a negativeclassification value, respectively.

Thereafter, a determination is made as to whether any unselecteddescriptors remain (operation 710). If any unselected descriptorsremain, the process returns to operation 704 described above. Otherwise,a determination is made as to whether any unselected frequencydistribution functions remain (operation 712). If any unselectedfrequency distribution functions remain, the process returns tooperation 702 described above.

Otherwise, the process generates a classification output that identifiesa classification result for each frequency distribution function of theplurality of frequency distribution functions (operation 714), with theprocess terminating thereafter. In operation 714, the classificationresult for a particular frequency distribution function identifieswhether that frequency distribution function represents zero, one, two,three, four, five, or some other number of modes of structural change.

With reference now to FIG. 8, an illustration of a process for analyzinga composite object in an aircraft using acoustic emissions is depictedin the form of a flowchart in accordance with an illustrativeembodiment. The process illustrated in FIG. 8 may be implemented usingacoustic sensing system 204 and analyzer module 214 described in FIG. 2.

The process may begin by detecting acoustic emissions emanating from acomposite object in an aircraft using an acoustic sensing system togenerate acoustic waveform data (800). Next, the acoustic waveform dataand load data for the object is received at an analyzer module (802).

Thereafter, a plurality of bins is created, by the analyzer module, forthe load data (operation 804). In operation 804, depending on theimplementation, the plurality of bins may be a plurality of time bins ora plurality of load bins.

A plurality of frequency distribution functions is then generated, bythe analyzer module, for the plurality of bins using the acousticwaveform data (operation 806). In operation 806, a frequencydistribution function is generated for each bin in the plurality ofbins. In some illustrative examples, the plurality of frequencydistribution functions take the form of a plurality of frequencyhistograms.

Thereafter, a set of learning algorithms is applied to the plurality offrequency distribution functions, the acoustic waveform data, and astored plurality of descriptors for a previously generated plurality ofclusters of frequency distribution functions to generate aclassification output that allows an operator to more easily and quicklyassess a structural integrity of the composite object in which theclassification output identifies a classification result for eachwaveform in the acoustic waveform data (operation 808), with the processterminating thereafter. In operation 808, the classification result mayidentify a particular waveform as representing zero, one, two, three,four, or some other number of modes of structural change.

The flowcharts and block diagrams in the different depicted embodimentsillustrate the architecture, functionality, and operation of somepossible implementations of apparatuses and methods in an illustrativeembodiment. In this regard, each block in the flowcharts or blockdiagrams may represent a module, a segment, a function, and/or a portionof an operation or step.

In some alternative implementations of an illustrative embodiment, thefunction or functions noted in the blocks may occur out of the ordernoted in the figures. For example, in some cases, two blocks shown insuccession may be executed substantially concurrently, or the blocks maysometimes be performed in the reverse order, depending upon thefunctionality involved. Also, other blocks may be added in addition tothe illustrated blocks in a flowchart or block diagram.

Turning now to FIG. 9, an illustration of a data processing system inthe form of a block diagram is depicted in accordance with anillustrative embodiment. Data processing system 900 may be used toimplement analyzer module 214, computer system 216, or both in FIG. 2.As depicted, data processing system 900 includes communicationsframework 902, which provides communications between processor unit 904,storage devices 906, communications unit 908, input/output unit 910, anddisplay 912. In some cases, communications framework 902 may beimplemented as a bus system.

Processor unit 904 is configured to execute instructions for software toperform a number of operations. Processor unit 904 may comprise a numberof processors, a multi-processor core, and/or some other type ofprocessor, depending on the implementation. In some cases, processorunit 904 may take the form of a hardware unit, such as a circuit system,an application specific integrated circuit (ASIC), a programmable logicdevice, or some other suitable type of hardware unit.

Instructions for the operating system, applications, and/or programs runby processor unit 904 may be located in storage devices 906. Storagedevices 906 may be in communication with processor unit 904 throughcommunications framework 902. As used herein, a storage device, alsoreferred to as a computer readable storage device, is any piece ofhardware capable of storing information on a temporary and/or permanentbasis. This information may include, but is not limited to, data,program code, and/or other information.

Memory 914 and persistent storage 916 are examples of storage devices906. Memory 914 may take the form of, for example, a random accessmemory or some type of volatile or non-volatile storage device.Persistent storage 916 may comprise any number of components or devices.For example, persistent storage 916 may comprise a hard drive, a flashmemory, a rewritable optical disk, a rewritable magnetic tape, or somecombination of the above. The media used by persistent storage 916 mayor may not be removable.

Communications unit 908 allows data processing system 900 to communicatewith other data processing systems and/or devices. Communications unit908 may provide communications using physical and/or wirelesscommunications links.

Input/output unit 910 allows input to be received from and output to besent to other devices connected to data processing system 900. Forexample, input/output unit 910 may allow user input to be receivedthrough a keyboard, a mouse, and/or some other type of input device. Asanother example, input/output unit 910 may allow output to be sent to aprinter connected to data processing system 900.

Display 912 is configured to display information to a user. Display 912may comprise, for example, without limitation, a monitor, a touchscreen, a laser display, a holographic display, a virtual displaydevice, and/or some other type of display device.

In this illustrative example, the processes of the differentillustrative embodiments may be performed by processor unit 904 usingcomputer-implemented instructions. These instructions may be referred toas program code, computer usable program code, or computer readableprogram code and may be read and executed by one or more processors inprocessor unit 904.

In these examples, program code 918 is located in a functional form oncomputer readable media 920, which is selectively removable, and may beloaded onto or transferred to data processing system 900 for executionby processor unit 904. Program code 918 and computer readable media 920together form computer program product 922. In this illustrativeexample, computer readable media 920 may be computer readable storagemedia 924 or computer readable signal media 926.

Computer readable storage media 924 is a physical or tangible storagedevice used to store program code 918 rather than a medium thatpropagates or transmits program code 918. Computer readable storagemedia 924 may be, for example, without limitation, an optical ormagnetic disk or a persistent storage device that is connected to dataprocessing system 900.

Alternatively, program code 918 may be transferred to data processingsystem 900 using computer readable signal media 926. Computer readablesignal media 926 may be, for example, a propagated data signalcontaining program code 918. This data signal may be an electromagneticsignal, an optical signal, and/or some other type of signal that can betransmitted over physical and/or wireless communications links.

The illustration of data processing system 900 in FIG. 9 is not meant toprovide architectural limitations to the manner in which theillustrative embodiments may be implemented. The different illustrativeembodiments may be implemented in a data processing system that includescomponents in addition to or in place of those illustrated for dataprocessing system 900. Further, components shown in FIG. 9 may be variedfrom the illustrative examples shown.

Illustrative embodiments of the disclosure may be described in thecontext of aircraft manufacturing and service method 1000 as shown inFIG. 10 and aircraft 1100 as shown in FIG. 11. Turning first to FIG. 10,an illustration of an aircraft manufacturing and service method isdepicted in the form of a block diagram in accordance with anillustrative embodiment. During pre-production, aircraft manufacturingand service method 1000 may include specification and design 1002 ofaircraft 1100 in FIG. 11 and material procurement 1004.

During production, component and subassembly manufacturing 1006 andsystem integration 1008 of aircraft 1100 in FIG. 11 takes place.Thereafter, aircraft 1100 in FIG. 11 may go through certification anddelivery 1010 in order to be placed in service 1012. While in service1012 by a customer, aircraft 1100 in FIG. 11 is scheduled for routinemaintenance and service 1014, which may include modification,reconfiguration, refurbishment, and other maintenance or service.

Each of the processes of aircraft manufacturing and service method 1000may be performed or carried out by a system integrator, a third party,and/or an operator. In these examples, the operator may be a customer.For the purposes of this description, a system integrator may include,without limitation, any number of aircraft manufacturers andmajor-system subcontractors; a third party may include, withoutlimitation, any number of vendors, subcontractors, and suppliers; and anoperator may be an airline, a leasing company, a military entity, aservice organization, and so on.

With reference now to FIG. 11, an illustration of an aircraft isdepicted in which an illustrative embodiment may be implemented. In thisexample, aircraft 1100 is produced by aircraft manufacturing and servicemethod 1000 in FIG. 10 and may include airframe 1102 with systems 1104and interior 1106. Examples of systems 1104 include one or more ofpropulsion system 1108, electrical system 1110, hydraulic system 1112,and environmental system 1114. Any number of other systems may beincluded. Although an aerospace example is shown, different illustrativeembodiments may be applied to other industries, such as the automotiveindustry.

Apparatuses and methods embodied herein may be employed during at leastone of the stages of aircraft manufacturing and service method 1000 inFIG. 10. In particular, acoustic sensing system 204 and analyzer module214 from FIG. 2 may be used during any one of the stages of aircraftmanufacturing and service method 1000.

For example, without limitation, acoustic sensing system 204 from FIG. 2may be used to detect acoustic emissions from various parts in aircraft1100 during at least one of component and subassembly manufacturing1006, system integration 1008, in service 1012, routine maintenance andservice 1014, or some other stage of aircraft manufacturing and servicemethod 1000. Still further, analyzer module 214 from FIG. 2 may be usedto analyze detected acoustic emissions during at least one of componentand subassembly manufacturing 1006, system integration 1008, in service1012, routine maintenance and service 1014, or some other stage ofaircraft manufacturing and service method 1000.

In one illustrative example, components or subassemblies produced incomponent and subassembly manufacturing 1006 in FIG. 10 may befabricated or manufactured in a manner similar to components orsubassemblies produced while aircraft 1100 is in service 1012 in FIG.10. As yet another example, one or more apparatus embodiments, methodembodiments, or a combination thereof may be utilized during productionstages, such as component and subassembly manufacturing 1006 and systemintegration 1008 in FIG. 10. One or more apparatus embodiments, methodembodiments, or a combination thereof may be utilized while aircraft1100 is in service 1012 and/or during maintenance and service 1014 inFIG. 10. The use of a number of the different illustrative embodimentsmay substantially expedite the assembly of and/or reduce the cost ofaircraft 1100.

An apparatus comprises an acoustic sensing system and an analyzermodule. The acoustic sensing system is positioned relative to an object.The acoustic sensing system detects acoustic emissions and generatesacoustic waveform data for the acoustic emissions detected. The analyzermodule is implemented in a computer system. The analyzer module receivesload data and the acoustic waveform data for the object, creates aplurality of bins for the load data, generates a plurality of frequencydistribution functions for the plurality of bins using the acousticwaveform data, and applies a set of learning algorithms to the pluralityof frequency distribution functions and the acoustic waveform data togenerate an output that allows an operator to more easily and quicklyassess a structural integrity of the object.

In some illustrative examples, an acoustic sensor positioned in contactwith the object. In some illustrative examples, the set of learningalgorithms includes either a set of unsupervised learning algorithms ora set of supervised learning algorithms. In some illustrative examples,the load data is either retrieved from a database or received from aload sensing system that measures a loading of the object as theacoustic waveform data is generated.

In some illustrative examples, the plurality of bins have a plurality ofbin widths and wherein a bin width in the plurality of bin widths iseither a time interval or a load interval. In some illustrativeexamples, each frequency distribution function in the plurality offrequency distribution functions has a plurality of bin widths in whicheach bin width of the plurality of bin widths is a defined frequencyinterval. In some illustrative examples, each frequency distributionfunction in the plurality of frequency distribution functions comprisesa plurality of frequency bins and wherein a frequency bin in theplurality of frequency bins includes either a count of a number offrequency peaks that fall within the frequency bin or an accumulation ofenergy at the frequency bin computed using the acoustic waveform data.

In some illustrative examples, the output identifies a plurality ofclusters of the plurality of frequency distribution functions. In theseillustrative examples, the plurality of clusters comprises: a firstcluster representing a first mode of structural change; a second clusterrepresenting a second mode of structural change; a third clusterrepresenting a third mode of structural change; and a fourth clusterrepresenting a fourth mode of structural change.

In some illustrative examples, the analyzer module applies the set oflearning algorithms to the plurality of frequency distribution functionsto establish a plurality of clusters and to identify a plurality ofdescriptors for the plurality of clusters. In some of these illustrativeexamples, the plurality of clusters is analyzed with alternate test datato associate each descriptor in the plurality of descriptors with adifferent mode of structural change and wherein the alternate test datais selected from one of x-ray imaging data, ultrasound imaging data,infrared imaging data, and modeling data. In some of these illustrativeexamples, the analyzer module generates a descriptor classificationoutput that associates a mode of structural change with the eachdescriptor in the plurality of descriptors and wherein the descriptorclassification output is stored in a database for future use inevaluating a structural integrity of a part during at least one stage ina lifecycle of the part.

A method is provided for analyzing an object using acoustic waves. Loaddata is received for the object. Acoustic waveform data is received forthe object from an acoustic sensing system. The acoustic waveform datarepresents acoustic emissions emanating from the object and is detectedusing the acoustic sensing system. A plurality of bins is created forthe load data. A plurality of frequency distribution functions isgenerated for the plurality of bins using the acoustic waveform data. Aset of learning algorithms is applied to the plurality of frequencydistribution functions and the acoustic waveform data to generate anoutput that allows an operator to more easily and quickly assess astructural integrity of the object. In some illustrative examples, themethod further comprises detecting, by the acoustic sensing system,acoustic waves radiating from the object using at least one acousticsensor to generate an acoustic emissions signal; and converting theacoustic emissions signal into the acoustic waveform data.

In some illustrative examples, creating the plurality of bins comprises:identifying a plurality of bin widths for the plurality of bins, whereina bin width in the plurality of bin widths is either a defined timeinterval or a defined load interval; and identifying a set of waveformsin the acoustic waveform data that fall within each bin of the pluralityof bins. In some of these illustrative examples, generating theplurality of frequency distribution functions comprises: dividing aselected frequency range into a plurality of frequency bins based on atleast one defined frequency interval. In some of these illustrativeexamples, generating the plurality of frequency distribution functionsfurther comprises: computing a Fast Fourier Transform for the set ofwaveforms that fall within a bin selected from the plurality of bins;and updating the plurality of frequency bins in a frequency distributionfunction for the bin based on the Fast Fourier Transform. In some ofthese illustrative examples, updating the plurality of frequency binscomprises: selecting a number of frequency peaks for each waveform inthe set of waveforms; and incrementing a frequency bin in the pluralityof frequency bins when a frequency peak in the number of frequency peakshas a frequency that falls within the frequency bin.

In some illustrative examples, applying the set of learning algorithmscomprises: applying a set of unsupervised learning algorithms to theplurality of frequency distribution functions and the acoustic waveformdata to establish a plurality of clusters; identifying a plurality ofdescriptors for the plurality of clusters; and generating a descriptorclassification output that classifies each descriptor in the pluralityof descriptors as representing a different mode of structural changebased on a plurality of modes identified using alternate test data. Insome illustrative examples, applying the set of learning algorithmscomprises: applying a set of supervised learning algorithms to theplurality of frequency distribution functions, the acoustic waveformdata, and a stored plurality of descriptors; and generating aclassification result for each frequency distribution function of theplurality of frequency distribution functions.

A method is provided for monitoring a composite object in an aircraftduring at least one stage in a lifecycle of the aircraft. Acousticsemissions radiating from the composite object are detected using anacoustic sensing system to generate acoustic waveform data. An analyzermodule receives the acoustic waveform data and load data for thecomposite object. The analyzer module creates a plurality of bins forthe load data. A set of waveforms in the acoustic waveform data fallswithin a corresponding bin in the plurality of bins. The analyzer modulegenerates a plurality of frequency distribution functions for theplurality of bins using the acoustic waveform data. A set of supervisedlearning algorithms is applied to the plurality of frequencydistribution functions, the acoustic waveform data, and a storedplurality of descriptors to generate a classification output thatidentifies a classification result for each frequency distributionfunction in the plurality of frequency distribution functions. Theclassification output allows an operator to more easily and quicklyassess a structural integrity of the composite object.

In some illustrative examples, applying the set of supervised learningalgorithms to the plurality of frequency distribution functions, theacoustic waveform data, and the stored plurality of descriptors togenerate the classification output comprises: generating theclassification result for a selected frequency distribution function inthe plurality of frequency distribution functions by comparing theselected frequency distribution function to each of the stored pluralityof descriptors, wherein the classification result identifies whether theselected frequency distribution function, and thereby a correspondingset of waveforms in the acoustic waveform data, represents zero or moremodes of structural change.

Turning now to FIG. 12, an illustration of a frequency distributionfunction time evolution image is depicted in accordance with anillustrative embodiment. Frequency distribution function time evolutionimage 1200 is an example of an image formed with a plurality of pointsof each of plurality of frequency distribution functions 228 of FIG. 2.As used herein, a “plurality” of items is more than one item. Forexample, a plurality of points is more than one point. When a pluralityof points of each of plurality of frequency distribution functions 228is used to form frequency distribution function time evolution image1200, more than one point of each of plurality of frequency distributionfunctions 228 is used to form frequency distribution function timeevolution image 1200. In some illustrative examples, frequencydistribution function time evolution image 1200 is an example of animage formed with all data of each of plurality of frequencydistribution functions 228 of FIG. 2.

Frequency distribution function time evolution image 1200 may be used totune parameters used to create plurality of frequency distributionfunctions 228 of FIG. 2. Frequency distribution function time evolutionimage 1200 may be used to determine changes to an object or assess astructural integrity of the object, such as object 200 of FIG. 2.

Frequency distribution function time evolution image 1200 has x-axis1202 and y-axis 1204. Y-axis 1204 is a frequency. As depicted, x-axis1202 is time. In other illustrative examples, x-axis 1202 is load.

Legend 1206 identifies the correlation between amplitude and color foreach pixel of frequency distribution function time evolution image 1200.Each column of frequency distribution function time evolution image 1200contains the data of a single frequency distribution function.

In some illustrative examples, it is desirable to identify values for atleast one of plurality of bin widths 226, plurality of frequency bins235, a quantity of frequency peaks in frequency peaks 240 of FIG. 2, anda quantity of plurality of clusters 242 of FIG. 2. The illustration ofobject 200, acoustic sensing system 204, and analyzer module 214 in FIG.2 is not meant to imply physical or architectural limitations to themanner in which an illustrative embodiment may be implemented. Thedifferent components shown in FIGS. 1-3 may be combined with componentsin FIGS. 12-16, used with components in FIGS. 12-16, or a combination ofthe two.

Turning now to FIG. 13, an illustration of an object, an acousticsensing system, and an analyzer module in the form of a block diagram isdepicted in accordance with an illustrative embodiment. FIG. 13 includesobject 200, acoustic sensing system 204, and analyzer module 214previously shown in FIG. 2. FIG. 13 also contains components used informing or analyzing components of FIG. 2.

In some illustrative examples, analyzer module 214 generates frequencydistribution function time evolution image 1300 using acoustic waveformdata 212 for object 200. In some illustrative examples, frequencydistribution function time evolution image 1300 is displayed on displaysystem 252. When displayed, frequency distribution function timeevolution image 1300 allows an operator to assess a structural integrityof object 200.

Data of each of plurality of frequency distribution functions 228 iscontained in frequency distribution function time evolution image 1300.Frequency distribution function time evolution image 1200 of FIG. 12 isa physical implementation of frequency distribution function timeevolution image 1300.

Frequency distribution function time evolution image 1300 has x-axis1302 of either time or load. Frequency distribution function timeevolution image 1300 has y-axis 1304 of frequency. Frequencydistribution function time evolution image 1300 identifies amplitude1306 using one of color 1308 or saturation 1310.

Frequency distribution function time evolution image 1300 is a displayof array 1312. Array 1312 has quantity of columns 1314 and quantity ofrows 1316. Array 1312 has quantity of columns 1314 equivalent toquantity 1318 of frequency distribution functions in plurality offrequency distribution functions 228. Array 1312 has quantity of rows1316 equivalent to quantity of frequency bins 1320 in each frequencydistribution function of plurality of frequency distribution functions228. Each frequency distribution function of plurality of frequencydistribution functions 228 has identifier 1322. Identifier 1322 is aunique range to a respective frequency distribution function ofplurality of frequency distribution functions 228. This range may beexpressed as either time 1324 or load 1326.

When identifier 1322 is time 1324, plurality of bin widths 226 of FIG. 2is a plurality of time-based bin widths. In other words, each bin ofplurality of bins 224 of FIG. 2 may correspond to a time interval. Whenidentifier 1322 is load 1326, plurality of bin widths 226 of FIG. 2 is aplurality of load-based bin widths.

Array 1312 is filled such that a plurality of points of each ofplurality of frequency distribution functions 228 is contained withinarray 1312. In some illustrative examples, array 1312 is filled suchthat all data of each of plurality of frequency distribution functions228 is contained with array 1312. Array 1312 allows analyzer module 214to identify structural changes in object 200 previously unidentifiableby an operator using frequency plots containing a single point extractedfrom each waveform of acoustic waveform data 212.

Each entry of array 1312 correlates to a sample of one of the respectivefrequency distribution functions of plurality of frequency distributionfunctions 228. Entry 1328 has row value 1330 and column value 1332. Rowvalue 1330 indicates the row for entry 1328. Column value 1332 indicatesthe column for entry 1328. When row value 1330 is the xth row and columnvalue 1332 is the yth column, entry 1328 contains the value of the ythfrequency bin of the xth frequency distribution function.

Plurality of frequency distribution functions 228 is created usingparameters including width of frequency bins 1334, value of bin width1336, and quantity of frequency peaks 1338. In some illustrativeexamples, at least one of width of frequency bins 1334, value of binwidth 1336, and quantity of frequency peaks 1338 is selected by anoperator.

Each of width of frequency bins 1334, value of bin width 1336, andquantity of frequency peaks 1338 may be tuned using parameter tuning1340. Parameter tuning 1340 is iterative 1342 and may be referred to asiterative parameter tuning.

In some illustrative examples, at least one of width of frequency bins1334, value of bin width 1336, and quantity of frequency peaks 1338 isidentified using parameter tuning 1340 and plurality of frequencydistribution function time evolution images 1344 generated from acousticwaveform data 212. Parameter tuning 1340 generates plurality offrequency distribution function time evolution images 1344 for aplurality of values of a parameter of interest. Difference in images1346 is determined between each successive pair of plurality offrequency distribution function time evolution images 1344. A quantityfor the parameter of interest is selected from difference in images1346.

In some illustrative examples, a quantity for a parameter of interest isselected from a range of operator provided values. For example, anoperator may provide a range of values for each of width of frequencybins 1334, value of bin width 1336, and quantity of frequency peaks 1338to receive parameter tuning 1340. Parameter tuning 1340 is performed bygenerating plurality of frequency distribution function time evolutionimages 1344 for every combination of values within the provided rangesfor the parameters. When only a single parameter of interest receivesparameter tuning 1340, plurality of frequency distribution function timeevolution images 1344 is generated using each value in the respectiverange of values for the parameter of interest.

In some illustrative examples, two parameters of width of frequency bins1334, value of bin width 1336, and quantity of frequency peaks 1338 aretuned using parameter tuning 1340. In these illustrative examples, thethird parameter is kept at a constant value. After tuning the twoparameters, the two values determined through parameter tuning 1340 areused to tune the last parameter using parameter tuning 1340.

Width of frequency bins 1334, value of bin width 1336, and quantity offrequency peaks 1338 may be tuned using parameter tuning 1340 in anydesirable order. In some illustrative examples, the order for parametertuning 1340 is influenced by the material forming object 200. In someillustrative examples, a desirable range for at least one of width offrequency bins 1334, value of bin width 1336, or quantity of frequencypeaks 1338 is affected by the material of object 200. In someillustrative examples, the size of a range for at least one of width offrequency bins 1334, value of bin width 1336, or quantity of frequencypeaks 1338 is affected by the material of object 200. For example, whenobject 200 is composite object 202, acoustic emissions have a frequencyin the range of 100-1000 kHz. Due to the acoustic emissions frequencyrange of composite object 202, a range for width of frequency bins 1334is limited. In some illustrative examples, when object 200 is compositeobject 202, width of frequency bins 1334 is tuned after tuning value ofbin width 1336 and quantity of frequency peaks 1338 using parametertuning 1340. In some of these illustrative examples, width of frequencybins 1334 is set at a value in the range of 20-50 kHz to initially tunevalue of bin width 1336 and quantity of frequency peaks 1338.

In some illustrative examples, a quantity for a parameter of interest isselected within a difference in terms of percentage at a steady statecondition. For example, the steady state condition may be 5% differencebetween a pair of successive images. As another example, the steadystate condition may be 13% difference between a pair of successiveimages. In some illustrative examples, a quantity for a parameter ofinterest is selected when the change is within 10% of a change of steadystate. For example, if the steady state condition is 8% differencebetween a pair of successive images, a value for the parameter ofinterest may be selected when the difference value is between 18% andthe steady state of 8%. As another example, if the steady statecondition is 20% difference between a pair of successive images, a valuefor the parameter of interest may be selected when the difference valueis between 30% and the steady state of 20%.

In some illustrative examples, analyzer module 214 selects quantity ofclusters 1348. In some illustrative examples, analyzer module 214identifies quantity of clusters 1348 using singular value decomposition1350 of frequency distribution function time evolution image 1300. Insome illustrative examples, determining quantity of clusters 1348comprises selecting quantity of clusters 1348 from entries within acurved portion of a plot of the singular values from singular valuedecomposition 1350 on frequency distribution function time evolutionimage 1300.

Turning now to FIG. 14, an illustration of a difference in change graphthat may be used to tune parameters is depicted in accordance with anillustrative embodiment. Difference in change graph 1400 is a physicalillustration of difference in images 1346 of FIG. 13. Difference inchange graph 1400 is an example of a graph used in parameter tuning 1340of FIG. 13. Any desirable parameter of width of frequency bins 1334,value of bin width 1336, or quantity of frequency peaks 1338 of FIG. 13may be tuned using a difference in change graph, such as difference inchange graph 1400.

As depicted, difference in change graph 1400 is a physicalimplementation of a graph that may be used to tune quantity of frequencypeaks 1338 of FIG. 13. Difference in change graph 1400 has x-axis 1402of quantity of peaks 1404. Difference in change graph 1400 has y-axis1406 of difference in change between successive frequency distributionfunction time evolution images.

In difference in change graph 1400, legend 1408 shows the length of timewindow for testing. The length of time window is a width of theplurality of frequency bins, such as width of frequency bins 1334 ofFIG. 13. The length of time window is a width of plurality of frequencybins 235 of FIG. 2. As depicted, each curve of curves 1410 reachessteady state around 10%. When the change is within 10% of change ofsteady state, the value for quantity of peaks 1404 may be selected. Asdepicted, each of curves 1410 reaches within 10% of change of steadystate between 8 peaks and 10 peaks. A value for quantity of frequencypeaks 1338 of FIG. 13 may desirably be in the range of 8 to 10 peaks. Inother illustrative examples, other desirable quantities of peaks may bedetermined depending on the applicable difference in change graph.

In some illustrative examples, width of frequency bins 1334 of FIG. 13may be decided from FIG. 14. In some illustrative examples, options forwidth of frequency bins 1334 of FIG. 13 may be eliminated using FIG. 14.For example, window=0.1 seconds and window=0.05 seconds may beeliminated from FIG. 14. Window=0.1 seconds and window=0.05 seconds aresubstantially on top of one another. If a plot of the difference as afunction of frequency bin width is created, the difference would be atsteady state by frequency bin size 0.05.

Turning now to FIG. 15, an illustration of a frequency distributionfunction time evolution image beside a normalized loading curve isdepicted in accordance with an illustrative embodiment. View 1500includes frequency distribution function time evolution image 1502 andnormalized loading curve 1504. Normalized loading curve 1504 includesdip 1506 and dip 1508. Each of dip 1506 and dip 1508 indicates changesto the object being tested.

Normalized loading curve 1504 may be used to perform a validation stepconfirming that all instances of material changes in normalized loadingcurve 1504 are also present in frequency distribution function timeevolution image 1502. Frequency distribution function time evolutionimage 1502 captures changes displayed in normalized loading curve 1504and additional changes not detected by normalized loading curve 1504.

In some illustrative examples, clusters, such as plurality of clusters242 of FIG. 2, may be selected by an operator based on at least one ofnormalized loading curve 1504 or frequency distribution function timeevolution image 1502. In some illustrative examples, a quantity ofclusters, such as quantity of clusters 1348 of FIG. 13 may be selectedby an operator based on at least one of normalized loading curve 1504 orfrequency distribution function time evolution image 1502.

As depicted, four clusters, clusters 1510, are separated by dashed lines1512. As depicted, dashed lines 1512 extend through dip 1506 and dip1508.

In some illustrative examples, a quantity of clusters, such as quantityof clusters 1348, may be selected automatically by an analyzer module,such as analyzer module 214 of FIGS. 2 and 13. When an analyzer moduleselects a quantity of clusters, the analyzer module performscalculations using data from frequency distribution function timeevolution image 1502.

Turning now to FIG. 16, an illustration of a plot of singular valuesfrom singular value decomposition of a frequency distribution functiontime evolution image is depicted in accordance with an illustrativeembodiment. In some illustrative examples, plot of singular values 1600is a physical implementation of results of singular value decomposition1350 of FIG. 13. Plot of singular values 1600 may be used to selectquantity of clusters 1348 of FIG. 13. Analyzer module 214 of FIGS. 2 and13 may perform calculations to create plot of singular values 1600 anddetermine quantity of clusters 1348. A quantity of clusters is selectedafter selecting each of width of frequency bins 1334, value of bin width1336, and quantity of frequency peaks 1338.

Plot of singular values 1600 has x-axis 1602 and y-axis 1604. X-axis1602 is a series of whole numerals for a quantity of clusters. Y-axis1604 is singular values 1606 from singular value decomposition offrequency distribution function time evolution image 1502 of FIG. 15.

As depicted, line 1608 extends through plurality of points 1610. Line1608 has curved portion 1612. In some illustrative examples, thequantity of clusters is selected from entries of plurality of points1610 within curved portion 1612 of plot of singular values 1600.

Turning now to FIG. 17, an illustration of a flowchart of a method foranalyzing an object using acoustic waves is depicted in accordance withan illustrative embodiment. Method 1700 may be implemented in testenvironment 100 of FIG. 1. Method 1700 may be implemented using acousticsensing system 204 and analyzer module 214 of computer system 216 ofFIGS. 2 and 13.

Method 1700 is a method for analyzing an object using acoustic waves.Method 1700 receives load data for the object (operation 1702). Method1700 receives acoustic waveform data for the object from an acousticsensing system, wherein the acoustic waveform data represents acousticemissions emanating from the object and is detected using the acousticsensing system (operation 1704). Method 1700 generates a plurality offrequency distribution functions using the acoustic waveform data(operation 1706). Method 1700 generates a frequency distributionfunction time evolution image containing a plurality of points of eachof the plurality of frequency distribution functions (operation 1708).Afterwards, method 1700 terminates.

In some illustrative examples, method 1700 generates a frequencydistribution function time evolution image containing all data of eachof the plurality of frequency distribution functions. In someillustrative examples, the frequency distribution function timeevolution image allows an operator to assess a structural integrity ofthe object. In some illustrative examples, the frequency distributionfunction time evolution image is used by an analyzer module to assessthe structural integrity of the object. In some illustrative examples,the frequency distribution function time evolution image or the datacontained in the frequency distribution function time evolution imageallows the analyzer module to identify structural changes in the objectpreviously unidentifiable by an operator using frequency plotscontaining a single point for each waveform of the acoustic waveformdata.

In some illustrative examples, method 1700 identifies a value of binwidth for a plurality of bins for the plurality of frequencydistribution functions, wherein the value of bin width is either adefined time interval or a defined load interval (operation 1710). Insome of these illustrative examples, identifying the value of bin widthfor the plurality of bins for the plurality of frequency distributionfunctions comprises identifying the value of bin width using iterativeparameter tuning and a plurality of frequency distribution function timeevolution images generated from the acoustic waveform data (operation1712). In some of these illustrative examples, identifying the value ofbin width for the plurality of bins for the plurality of frequencydistribution functions comprises identifying the value of bin width whena percentage difference between two successive frequency distributionfunction time evolution images of the plurality of frequencydistribution function time evolution images is within 10% of change of asteady state condition (operation 1714).

In some illustrative examples, method 1700 identifies at least one of awidth of frequency bins or a quantity of frequency peaks using iterativeparameter tuning and a plurality of frequency distribution function timeevolution images generated from the acoustic waveform data (operation1716).

In some illustrative examples, method 1700 determines a quantity ofclusters using the frequency distribution function time evolution image(operation 1718). In some of these illustrative examples, method 1700determines the quantity of clusters comprises determining the quantityof clusters by applying a singular value decomposition on the frequencydistribution function time evolution image (operation 1720). In some ofthese illustrative examples, determining the quantity of clusterscomprises selecting the quantity of clusters from entries within acurved portion of a plot of the singular values from the singular valuedecomposition on the frequency distribution function time evolutionimage (operation 1722).

Turning now to FIG. 18, an illustration of a flowchart of a method forcreating an array for analysis is depicted in accordance with anillustrative embodiment. Method 1800 may be implemented in testenvironment 100 of FIG. 1. Method 1800 may be implemented using acousticsensing system 204 and analyzer module 214 of computer system 216 ofFIGS. 2 and 13.

Method 1800 detects acoustic emissions radiating from an object using anacoustic sensing system to generate acoustic waveform data (operation1802). Method 1800 generates, by an analyzer module, a plurality offrequency distribution functions using the acoustic waveform data,wherein each of the plurality of frequency distribution functions has anidentifier of either a respective time or a respective load (operation1804). Method 1800 creates an array that has a quantity of columnsequivalent to a quantity of frequency distribution functions in theplurality of frequency distribution functions and a quantity of rowsequivalent to a quantity of frequency bins in each frequencydistribution function of the plurality of frequency distributionfunctions (operation 1806). Method 1800 fills the array such that aplurality of points of each of the plurality of frequency distributionfunctions is contained within the array, in which the array allows theanalyzer module to identify structural changes in the object previouslyunidentifiable by an operator using frequency plots containing a singlepoint for each waveform of the acoustic waveform data (operation 1808).Afterwards, method 1800 terminates.

In some illustrative examples, method 1800 fills the array such that alldata of each of the plurality of frequency distribution functions iscontained within the array. In some illustrative examples, method 1800displays the array as a frequency distribution function time evolutionimage in which each column of the image is representative of arespective frequency distribution function of the plurality of frequencydistribution functions (operation 1810). In some illustrative examples,in method 1800, each pixel of the frequency distribution function timeevolution image has a color or a saturation representative of anamplitude (operation 1812). In some illustrative examples, method 1800iteratively generates and analyzes a plurality of frequency distributionfunction time evolution images to tune at least one parameter of theparameters used to generate the plurality of frequency distributionfunctions, wherein the parameters include a quantity of frequency peaks,value of bin width, and width of frequency bins (operation 1814).

Turning now to FIG. 19, an illustration of a flowchart of a method forturning parameters is depicted in accordance with an illustrativeembodiment. Method 1900 may be implemented in test environment 100 ofFIG. 1. Method 1900 may be implemented using acoustic sensing system 204and analyzer module 214 of computer system 216 of FIGS. 2 and 13. Method1900 is a method for tuning a parameter of interest. Any desirableparameter of width of frequency bins 1334, value of bin width 1336, orquantity of frequency peaks 1338 of FIG. 13 may be tuned using method1900.

Method 1900 determines a numerical range of a parameter of interest(operation 1902). Method 1900 starts the parameter of interest at asmallest value of the numerical range, N_(f1), and initialize all otherparameters (operation 1904). Method 1900 creates a frequencydistribution function time evolution image for N_(fx), starting withN_(f1) (operation 1906). Method 1900 creates a frequency distributionfunction time evolution image for N_(f2) (operation 1908).

Method 1900 calculates a percentage difference by sum of the differencebetween each set of two images and dividing by the different between theimages for N_(f1) and N_(f2), whereΣ|FTE_(Nf=i)−FTE_(Nf=i−1)|/Σ|FTE_(Nf=2)−FTE_(Nf=1)|×100% (operation1910). Method 1900 determines if frequency distribution function timeevolution images for all values of N_(f) have been created (operation1912). If not, operations 1906 and 1908 are repeated until all values ofN_(f) have a frequency distribution function time evolution image.Method 1900 selects a value for the parameter in which a percentagedifference between two successive frequency distribution function timeevolution images of the plurality of frequency distribution functiontime evolution images is within 10% of change of a steady statecondition (operation 1914). Afterwards, method 1900 terminates.

In some illustrative examples, if the images are not the same size,method 1900 interpolates such that the images are the same size(operation 1916).

The flowcharts and block diagrams in the different depicted embodimentsillustrate the architecture, functionality, and operation of somepossible implementations of apparatus and methods in an illustrativeembodiment. In this regard, each block in the flowcharts or blockdiagrams may represent a module, a segment, a function, and/or a portionof an operation or step.

In some alternative implementations of an illustrative embodiment, thefunction or functions noted in the blocks may occur out of the ordernoted in the figures. For example, in some cases, two blocks shown insuccession may be executed substantially concurrently, or the blocks maysometimes be performed in the reverse order, depending upon thefunctionality involved. Also, other blocks may be added, in addition tothe illustrated blocks, in a flowchart or block diagram.

In some illustrative examples, not all blocks of method 1700, method1800, or method 1900 are performed. For example, operations 1710 through1722 of FIG. 17 are optional. As another example, operations 1810through 1814 of FIG. 18 are optional. As a further example, operation1916 of FIG. 19 is optional.

The description of the different illustrative embodiments has beenpresented for purposes of illustration and description, and is notintended to be exhaustive or limited to the embodiments in the formdisclosed. Many modifications and variations will be apparent to thoseof ordinary skill in the art. Further, different illustrativeembodiments may provide different features as compared to otherdesirable embodiments. The embodiment or embodiments selected are chosenand described in order to best explain the principles of theembodiments, the practical application, and to enable others of ordinaryskill in the art to understand the disclosure for various embodimentswith various modifications as are suited to the particular usecontemplated.

What is claimed is:
 1. An apparatus comprising: an acoustic sensingsystem positioned relative to an object, wherein the acoustic sensingsystem detects acoustic emissions and generates acoustic waveform datafor the acoustic emissions detected; and an analyzer module implementedin a computer system that receives load data and the acoustic waveformdata for the object, generates a plurality of frequency distributionfunctions using the acoustic waveform data, and generates a frequencydistribution function time evolution image containing a plurality ofpoints of each of the plurality of frequency distribution functions. 2.The apparatus of claim 1, wherein generating the frequency distributionfunction time evolution image allows an operator to assess a structuralintegrity of the object.
 3. The apparatus of claim 1, wherein theanalyzer module identifies a value of bin width for a plurality of binsfor the plurality of frequency distribution functions wherein the valueof bin width is either a defined time interval or a defined loadinterval.
 4. The apparatus of claim 3, wherein the analyzer moduleidentifies the value of bin width using iterative parameter tuning and aplurality of frequency distribution function time evolution imagesgenerated from the acoustic waveform data.
 5. The apparatus of claim 4,wherein the analyzer module identifies the value of bin width when apercentage difference between two successive frequency distributionfunction time evolution images of the plurality of frequencydistribution function time evolution images is within 10% of change of asteady state condition.
 6. The apparatus of claim 1, wherein theanalyzer module identifies at least one of a width of frequency bins ora quantity of frequency peaks using iterative parameter tuning and aplurality of frequency distribution function time evolution imagesgenerated from the acoustic waveform data.
 7. The apparatus of claim 1,wherein the analyzer module determines a quantity of clusters using thefrequency distribution function time evolution image.
 8. The apparatusof claim 7, wherein the analyzer module determines the quantity ofclusters by applying a singular value decomposition on the frequencydistribution function time evolution image.
 9. The apparatus of claim 8,wherein the quantity of clusters is selected from entries within acurved portion of a plot of the singular values from the singular valuedecomposition on the frequency distribution function time evolutionimage.
 10. A method for analyzing an object using acoustic waves, themethod comprising: receiving load data for the object; receivingacoustic waveform data for the object from an acoustic sensing system,wherein the acoustic waveform data represents acoustic emissionsemanating from the object and is detected using the acoustic sensingsystem; generating a plurality of frequency distribution functions usingthe acoustic waveform data; and generating a frequency distributionfunction time evolution image containing a plurality of points of eachof the plurality of frequency distribution functions.
 11. The method ofclaim 10, wherein the frequency distribution function time evolutionimage allows an operator to assess a structural integrity of the object.12. The method of claim 10 further comprising: identifying a value ofbin width for a plurality of bins for the plurality of frequencydistribution functions, wherein the value of bin width is either adefined time interval or a defined load interval.
 13. The method ofclaim 12, wherein identifying the value of bin width for the pluralityof bins for the plurality of frequency distribution functions comprisesidentifying the value of bin width using iterative parameter tuning anda plurality of frequency distribution function time evolution imagesgenerated from the acoustic waveform data.
 14. The method of claim 13,wherein identifying the value of bin width for the plurality of bins forthe plurality of frequency distribution functions comprises identifyingthe value of bin width when a percentage difference between twosuccessive frequency distribution function time evolution images of theplurality of frequency distribution function time evolution images iswithin 10% of change of a steady state condition.
 15. The method ofclaim 10 further comprising: identifying at least one of a width offrequency bins or a quantity of frequency peaks using iterativeparameter tuning and a plurality of frequency distribution function timeevolution images generated from the acoustic waveform data.
 16. Themethod of claim 10 further comprising: determining a quantity ofclusters using the frequency distribution function time evolution image.17. The method of claim 16, wherein determining the quantity of clusterscomprises determining the quantity of clusters by applying a singularvalue decomposition on the frequency distribution function timeevolution image.
 18. The method of claim 17, wherein determining thequantity of clusters comprises selecting the quantity of clusters fromentries within a curved portion of a plot of the singular values fromthe singular value decomposition on the frequency distribution functiontime evolution image.
 19. A method comprising: detecting acousticemissions radiating from an object using an acoustic sensing system togenerate acoustic waveform data; generating, by an analyzer module, aplurality of frequency distribution functions using the acousticwaveform data, wherein each of the plurality of frequency distributionfunctions has an identifier of either a respective time or a respectiveload; creating an array that has a quantity of columns equivalent to aquantity of frequency distribution functions in the plurality offrequency distribution functions and a quantity of rows equivalent to aquantity of frequency bins in each frequency distribution function ofthe plurality of frequency distribution functions; and filling the arraysuch that a plurality of points of each of the plurality of frequencydistribution functions is contained within the array, in which the arrayallows the analyzer module to identify structural changes in the objectpreviously unidentifiable by an operator using frequency plotscontaining a single point for each waveform of the acoustic waveformdata.
 20. The method of claim 19 further comprising: displaying thearray as a frequency distribution function time evolution image in whicheach column of the frequency distribution function time evolution imageis representative of a respective frequency distribution function of theplurality of frequency distribution functions.
 21. The method of claim20, wherein each pixel of the frequency distribution function timeevolution image has a color or a saturation representative of anamplitude.
 22. The method of claim 19 further comprising: iterativelygenerating and analyzing a plurality of frequency distribution functiontime evolution images to tune at least one parameter of the parametersused to generate the plurality of frequency distribution functions,wherein the parameters include a quantity of frequency peaks, value ofbin width, and width of frequency bins.